# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
#   LICENSE is in incl_licenses directory.

from __future__ import absolute_import, division, print_function, unicode_literals

import os
import argparse
import json
import torch
import numpy as np
import librosa
from utils import load_checkpoint
from meldataset import get_mel_spectrogram
from scipy.io.wavfile import write
from env import AttrDict
import tqdm
from meldataset import MAX_WAV_VALUE
# from bigvgan import BigVGAN as Generator
from bigcodec import BigCodecs as Generator

h = None
device = None
torch.backends.cudnn.benchmark = False


def inference(a, h):
    generator = Generator().to(device)

    state_dict_g = load_checkpoint(a.checkpoint_file, device)
    generator.load_state_dict(state_dict_g["generator"])
    #
    # filelist = os.listdir(a.input_wavs_dir)
    #
    # os.makedirs(a.output_dir, exist_ok=True)

    generator.eval()

    print(generator.get_emb()[0])
    np.save("bigcodec_codec.npy", generator.get_emb()[0].detach().cpu().numpy(), allow_pickle=False)

def main():
    print("Initializing Inference Process..")

    parser = argparse.ArgumentParser()
    parser.add_argument("--input_wavs_dir", default="test_files")
    parser.add_argument("--output_dir", default="generated_files")
    parser.add_argument("--checkpoint_file", required=True)
    # parser.add_argument("--use_cuda_kernel", action="store_true", default=False)

    a = parser.parse_args()

    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
    with open(config_file) as f:
        data = f.read()

    global h
    json_config = json.loads(data)
    h = AttrDict(json_config)

    torch.manual_seed(h.seed)
    global device
    if torch.cuda.is_available():
        torch.cuda.manual_seed(h.seed)
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    inference(a, h)


if __name__ == "__main__":
    main()
